Jianshe Kang

A New Improved Kurtogram and Its Application to Bearing Fault Diagnosis

A new improved Kurtogram was proposed in this paper. Instead of Kurtosis, correlated Kurtosis of envelope signal extracted from the wavelet packet node was used as an indicator to determine the optimal frequency band. Correlated Kurtosis helps to determine the fault related impulse signals not affected by other unrelated signal components. Finally, two simulated and three experimental bearing fault cases are used to validate the effectiveness of proposed method and to compare with other similar methods. The results demonstrate it can locate resonant frequency band with a high reliability than two previous developed methods by Lei et al. and Wang et al. especially for the incipient faults under low load.

Gearbox fault prognosis based on CHMM and SVM

A new gearbox fault prognosis scheme based on continuous hidden Markov model (CHMM) and support vector machine (SVM) is developed. Based on the features which are the energies of intrinsic mode functions (IMFs) decomposed by empirical mode decomposition (EMD) extracted from normal gearbox vibration signal, a CHMM is trained to model the normal condition. The logarithm of the probability of this CHMM is then used to detect any defects and assess their severity. Then, SVM is used to predict the value of new feature which is the logarithm of the probability. Experimental data collected from a gearbox degradation test is used to verify the efficacy of the new scheme.

Gearbox fault diagnosis method based on wavelet packet analysis and support vector machine

This paper presents an intelligent method for gear fault diagnosis based on wavelet packet analysis and support vector machine (SVM). For this purpose, two experiments were selected to verify the proposed method. One is a spur gear of the motorcycle gearbox system. Slight-worn, medium-worn, and broken-tooth were selected as the faults. In fault simulating, two very similar models of worn gear have been considered with partial difference for evaluating the preciseness of the proposed method. The other one is a helical gear of a gearbox system. Broken-tooth and crack in root of gear were selected as the faults. Raw vibration signals were segmented into the signals recorded during one complete revolution of the input shaft using tachometer information and then synchronized using cubic spline interpolation to construct the sample signals with the same length. Next, standard deviations of wavelet packet coefficients of the vibration signals which have been normalized and dimension deducted using principal component analysis (PCA) were considered as the feature vector for training purposes of the SVM. The parameters of SVM are optimized using particle swarm optimization (PSO). Its effectiveness is verified by experimental results.

Alpha Stable Distribution Based Morphological Filter for Bearing and Gear Fault Diagnosis in Nuclear Power Plant

Gear and bearing play an important role as key components of rotating machinery power transmission systems in nuclear power plants. Their state conditions are very important for safety and normal operation of entire nuclear power plant. Vibration based condition monitoring is more complicated for the gear and bearing of planetary gearbox than those of fixed-axis gearbox. Many theoretical and engineering challenges in planetary gearbox fault diagnosis have not yet been resolved which are of great importance for nuclear power plants. A detailed vibration condition monitoring review of planetary gearbox used in nuclear power plants is conducted in this paper. A new fault diagnosis method of planetary gearbox gears is proposed. Bearing fault data, bearing simulation data, and gear fault data are used to test the new method. Signals preprocessed using dilation-erosion gradient filter and fast Fourier transform for fault information extraction. The length of structuring element (SE) of dilation-erosion gradient filter is optimized by alpha stable distribution. Method experimental verification confirmed that parameter alpha is superior compared to kurtosis since it can reflect the form of entire signal and it cannot be influenced by noise similar to impulse.

A case study of bearing condition monitoring using SPM

Bearings consist one of the most widely used industrial rotating machine elements, especially rolling bearings and journal bearings. The capability to detect fast, accurately and easily the existence and severity of bearing fault during operation is very important as an unexpected failure of machine can lead to significant economic losses. This paper mainly introduces an actual case study of bearing condition monitoring in industry. As an effective tool in inspection the bearing condition, shock pulse method (SPM) is utilized to monitor the bearings of test rig which includes an electric motor and a wind turbine. Through analysing the frequency spectrum derived from the SPM instrument, the faulty bearing is located accurately. The case study validates that SPM is quite effective for monitoring the bearings condition in industrial applications.

Enhanced bearing fault detection and degradation analysis based on narrowband interference cancellation

In the condition based maintenance work of rotating machineries, bearings’ fault diagnosis and prognosis are an important content. Their faults can lead to many disasters. So, to detect the bearing faults earlier can benefit the remaining useful life (RUL) prediction and maintenance actions. In order to achieve this goal, narrowband interference cancellation (NIC) is used to extract the periodic impulsive signals which are indicative of a bearing fault. This method filters the narrowband signals not associated with the impulsive signal produced by bearing faults out. Therefore, the signal-to-noise will be improved and lead to the easier fault detection. However, only easier fault detection is not enough for RUL prediction. The features extracted from the vibration signals should reflect the bearings’ degradation well. This needs the features have good degradation trend (increase or decrease with time). In order to demonstrate the effectiveness of the proposed method, one implemented bearing fault test and one run-to-failure test are used to do the analysis. The results shows that bearing faults detection can be enhanced and root mean square (RMS) extracted from the NIC signal can track the bearing degradation well than the RMS extracted from the original vibration signal.

Fault diagnosis of gearbox based on EEMD and HMM

As a complicated mechanical component, gearbox plays a significant role in industrial field. Its fault diagnosis benefits decision making of maintenance and avoids undesired downtime cost. Empirical mode decomposition (EMD) is a self-adaptive signal processing method, which has been applied in non linear and non stationary signal processing successfully. However, the EMD algorithm has its inherent drawbacks. Aiming at the problem of intrinsic mode function (IMF) criterion in the EMD, this paper introduces the mode mixing problem of EMD in Hilbert-Huang Transform (HHT). In order to overcome the mode mixing problem in EMD, ensemble empirical mode decomposition (EEMD) is used. Therefore, this paper proposes a new method based on EEMD and hidden Markov mode (HMM) for gear fault diagnosis. First, a simulation signal is used to verify the advantages of EEMD comparing to EMD. Second, the new method is applied to the gear fault diagnosis. There are two patterns seeded faults in the experiment. One pattern is broken teeth, the other is cracks. The results show that the method can identify gear fault accurately and effectively.

Notice of Retraction Managing performance based contract of repairable system under three replacement policies

This paper investigates a discrete event simulation approach for planning performance based contract of repairable system. We focus on an integrated service delivery environment where the manufacturer develops capital-intensive systems and also provides after-sales support. We propose a simulation model to calculate system availability comprehending three performance drivers: life time distribution, repair time distribution, and spare parts inventory. This simulation model allows the service supplier to minimize the total cost across optimizing the system design, maintenance time, and spare part inventory level. In this simulation model, the failure time and repair time can follow arbitrary distribution. This will allow the customer to monitor the suppliers service through comparing the actual availability and theory availability. The expected costs per unit time under three replacement policies also are compared.